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Abstract

1. Antarctic krill (Euphausia superba) form aggregations known as swarms that vary greatly in size and density. Six acoustic surveys were conducted as part of multidisciplinary studies at two study sites, the western and eastern core boxes (WCB and ECB), during the 1997, 1998 and 1999 austral summers, at South Georgia. A quantitative, automated, image processing algorithm was used to identify swarms, and calculate swarm descriptors, or metrics. In contrast to acoustic surveys of aggregations of other pelagic species, a strong correlation (r = 0.88, p = 0.02, 95% C.I.= 0.24 to 0.99) between the number of krill swarms and the mean areal krill density [rho.hat] was found. Multivariate analysis was used to partition swarms into three types, based on contrasting morphological and internal krill density parameters. Swarm types were distributed differently between inter-surveys and between on and off-shelf regions.
This swarm type variation has implications for krill predators, by causing spatial heterogeneity in swarm detectability, suggesting that for optimal foraging to occur, predators must engage in some sort of adaptive foraging strategy.
2. Krill predator-prey interactions were found to occur at multiple spatial and temporal scales, in a nested, or hierarchical structure. At the largest inter-survey scale, an index of variability, I, was developed to compare variation in survey-scale predator sightings, sea temperature and [rho.hat]. Using I and a two-way ANOVA, core box, rather than year, was found to be a more important factor in determining species distribution. The absence of Blue-petrels (Halobaena caerulea) and the elevated number of Antarctic fur seals (Arctocephalus gazella) suggest that 1998 was a characterised by colder than average water surrounding South Georgia, and a high [rho.hat] in the ECB. At the smaller, intra-survey scales (<80 km, <5 day), the characteristic scale (distances in which
predator group size, or krill density were similar, L_s) were determined. For krill and predators L_s varied by survey and the L_s of krill also varied by depth within a survey. Overlap in L_s were stronger between predator species than between a predator species and krill, indicating predators were taking foraging cues from the activity of predators, rather than from the underlying krill distribution. No relationship was found between swarm characteristics and predator activity, suggesting either there is no relationship between krill swarms and predators, or that the predator and acoustic observation techniques may not be appropriate to detect such a relationship.
3. To overcome the 2-D sampling limitations of conventional echosounders, a multibeam echosounder (MBE) observed entire swarms in three-dimensions. Swarms found in the nearshore environment of Livingston Island situated in the South Shetland Islands, exhibited only a narrow range of surface area to volume ratios or roughnesses (R = 3.3, CV = 0.23), suggesting that krill adopt a consistent group behaviour to maintain swarm shape. Generalized additive models (GAM) suggested that the presence of air-breathing predators influenced the shape of a krill swarm (R decreased in the presence of predators: the swarm became more spherical). A 2D distance sampling framework was used to estimate the abundance, N, and associated variance of krill swarms. This technique took into account angular and range detectability (half-normal, [sigma_r.hat] = 365.00 m, CV = 0.16) and determined the vertical distribution of krill swarms to be best approximated by a beta-distribution ([alpha.hat] = 2.62, [CV.hat] = 0.19; [beta.hat] = 2.41, [CV.hat] = 0.15), giving the abundance of swarms in survey region as [N.hat] = 5,062 ([CV.hat] = 0.35). This research represents a substantial contribution to developing estimation of pelagic biomass using MBEs.
4. When using a single- or split-beam missing pings occur when the transmit or receive cycles are interrupted, often by aeration of the water column, under the echosounder transducer during rough weather. A thin-plate regression spline based approach was used to model the missing krill data, with knots chosen using a branch and bound algorithm. This method performs well for acoustic observations of krill swarms where data are tightly clustered and change rapidly. For these data the technique outperformed the standard MGCV GAM, and the technique is applicable for estimating acoustically derived biomass from line transect surveys.

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